from bluepyopt.ephys.responses import TimeVoltageResponse
from bluepyparallel import init_parallel_factory
import yaml
from functools import partial

from pathlib import Path
import matplotlib.pyplot as plt
import pickle

# from emodel_generalisation.utils import plot_traces
from emodel_generalisation.model.modifiers import synth_soma, synth_axon
from emodel_generalisation.mcmc import load_chains
from emodel_generalisation.mcmc import save_selected_emodels
import logging
from itertools import cycle
import json
from matplotlib.backends.backend_pdf import PdfPages
import pandas as pd
from emodel_generalisation.model.access_point import AccessPoint
from emodel_generalisation.model.evaluation import feature_evaluation
from emodel_generalisation.utils import get_combo_hash
from datareuse import Reuse
import extra_features


def plot_traces(trace_df, trace_path="traces", pdf_filename="traces.pdf"):
    """Plot traces from df, with highlighs on rows with trace_highlight = True.

    Args:
        trace_df (DataFrame): contains list of combos with traces to plot
        trace_path (str): path to folder with traces in .pkl
        pdf_filename (str): name of pdf to save
    """
    COLORS = cycle(["r"] + [f"C{i}" for i in range(10)])
    trace_df = trace_df.copy()  # prevents annyoing panda warnings
    if "trace_highlight" not in trace_df.columns:
        trace_df["trace_highlight"] = True
    for index in trace_df.index:
        if trace_df.loc[index, "trace_highlight"]:
            c = next(COLORS)

        if "trace_data" in trace_df.columns:
            trace_path = trace_df.loc[index, "trace_data"]
        else:
            combo_hash = get_combo_hash(trace_df.loc[index])
            trace_path = Path(trace_path) / ("trace_id_" + str(combo_hash) + ".pkl")

        with open(trace_path, "rb") as f:
            trace = pickle.load(f)
            if isinstance(trace, list):
                trace = trace[1]  # newer version the response are here
            for protocol, response in trace.items():
                lw = 0.8

                plt.figure(protocol, figsize=(10, 6))
                plt.gca().set_xlim(900, 1800)
                # if protocol == 'Step_ReboundBurst_burst.soma.ina':
                #    plt.gca().set_ylim(-60000, 0)
                plt.plot(
                    response["time"],
                    response["voltage"],
                    # label=label,
                    c="0.5",
                    lw=lw,
                )

                plt.xlabel("Time (ms)")
                plt.ylabel("Voltage (mV)")
    with PdfPages(pdf_filename) as pdf:
        for fig_id in plt.get_fignums():
            fig = plt.figure(fig_id)
            plt.legend(loc="best")
            plt.suptitle(fig.get_label())
            pdf.savefig()
            plt.close()


if __name__ == "__main__":
    parallel_factory = init_parallel_factory("multiprocessing")
    # parallel_factory = init_parallel_factory("dask_dataframe")
    emodel = "simplest"
    logger = logging.getLogger()
    v = 1
    logging.basicConfig(
        level=(logging.WARNING, logging.INFO, logging.DEBUG)[v],
        handlers=[logging.StreamHandler()],
    )
    logger.setLevel((logging.WARNING, logging.INFO, logging.DEBUG)[v])

    access_point = AccessPoint(
        emodel_dir=".",
        recipes_path="config/recipes.json",
        final_path="final.json",
        with_seeds=True,
    )
    exemplar_data = yaml.safe_load(open("../../../mcmc_run/exemplar_data.yaml"))
    access_point.morph_path = exemplar_data["paths"]["all"]
    access_point.settings["morph_modifiers"] = [
        partial(synth_soma, params=exemplar_data["soma"], scale=1.0),
        partial(synth_axon, params=exemplar_data["ais"]["popt"], scale=1.0),
    ]
    emodels = ["ecel", "spp", "runaway"]
    df = pd.DataFrame()
    for i, emodel in enumerate(emodels):
        df.loc[i, "emodel"] = f"simplest_{emodel}"
        df.loc[i, "name"] = f"exemplar_{emodel}"
    Path("traces").mkdir(exist_ok=True)
    with Reuse("eval_traces.csv") as reuse:
        df = reuse(
            feature_evaluation,
            df,
            access_point,
            parallel_factory=parallel_factory,
            trace_data_path="traces",
            # record_ions_and_currents=True,
        )
    for i, emodel in enumerate(emodels):
        plot_traces(df.loc[[i]], pdf_filename=f"traces_{emodel}.pdf")
        plt.close("all")
    from emodel_generalisation.utils import get_feature_df

    d = get_feature_df(df)
    plt.figure(figsize=(4, 3))
    currents = [0.05, 0.15, 0.25, 0.35, 0.45, 0.55]
    d[d.isna()] = 0
    plt.plot(currents, d.loc[0], "-+", label="ecel")
    plt.plot(currents, d.loc[1], "-+", label="spp")
    plt.plot(currents, d.loc[2], "-+", label="runaway")
    plt.legend()
    plt.xlabel("Step current [nA]")
    plt.ylabel("Mean frequency [Hz]")
    plt.axis([0, 0.58, 0, 150])
    plt.tight_layout()
    plt.savefig("if_curve.pdf")
